A Personal Perspective
During nearly twenty years of living in Portland, Oregon followed by a brief stint in New York and then eight years Denver, Colorado, I had the unique opportunity to see the practice of health care for low income people and families from two very unique perspectives. The first opportunity lasted from 1985 to 2002 in Oregon when I was one of many people there dependent upon the State’s welfare system followed by SSI and the Medicaid health care system. My next experience began in 2004 when I began my career as a biostatistician for the Medicaid, Medicare, CHP and employee health programs for a managed care company in Denver. Interspersed with these two periods was my experience in New York job hunting for two years.
During these years in Oregon, my experiences with the local Welfare and Oregon health care systems were quite varied. The management of health care provided to Welfare recipients in particular was quite an experience. In 1985, I had just finished ten years of live as a university student, earning two bachelors degrees followed by a little more than three years enrolled as a medical student.
My career track in medicine remained a goal until it came time for me to receive adequate care from the medical school due to worsening of my epilepsy following a several year history of inactivity. Such a need for assistance began as early as 1982, ten years before the Americans with Disabilities Act even came to be, six going on seven years into my college life, and just several months into my first year enrolled in an MD program.
During the three years period of 1982 to 1985 I learned how mismanaged and sometimes insensitive administrators for a medical school can be. Aside from the administrators there were also a number of teachers, doctors and other medical school officials who demonstrated their prejudice against the idea of treating people with certain disabilities how to be a doctor. The utopian vision of a physician embedded in these teachers’ minds then was that a doctor had to be heroic and faultless, someone who could be held responsible for every second of his or her lifespan whenever he or she is close to a patient, with the power to save a life as much as take one due to mistakes, inadequate knowledge and manual skills, or a lack of clarity with regard to his or her personal decision making processes.
The ideal doctor in 1982 was someone who was fully aware of his or her surroundings, who could at any moment in time or space be able to resuscitate a motionless patient, re-activate a patient’s dead heart, stop the anaphylaxis from suddenly setting in, or to assist the fetus waiting to come out after suddenly showing his or her face to the outside world for the first time. Their impression about epilepsy was that a seizure could be the reason any of the above necessary events to reviving a patient may not even happen. Never mind the fact that older doctors who are morbidly obese and are in charge of the region’s cardiology unit were thrice in need of cardiac surgery and several times incapable of providing care due to their own physical mishaps, or the fact that a physician in a wheel chair could be raised high enough to perform the CPR that was needed unless the patient was on the floor, or the fact that the 80 family physician with eyesight hampered due to his hypertension and diabetes still allowed to “see” his or her patients in spite of this late onset debilitation. With epilepsy, what mattered really about the rights for such a student to attend medical classes and clinical setting in order to learn and ultimately practice medicine, was whether he or she simply had epilepsy or not. If the former was the case, in 1982 like 50 or 100 years earlier, he or she was going to have to prove his/her ability to perform the necessary tasks, in spite of having a weekend long on-call responsibility or an overnight study and review event for the next course test.
Although engaged in quite silently, such a prejudice existed in doctors and the medical school setting back in 1982, to those whom such a philosophy was revealed, it just as much as it existed in the academic environment at some teaching hospital as it did in the society at large. The general perception of becoming a doctor was that only people with excellent health were allowed to learn to be physicians, or individuals with one or more of many physical limitations to life that could be effectively repaired, replaced or eliminated by the use of a prosthesis, a set of knee braces, or a well oiled wheelchair. There was also this conveyor belt like thinking prevalent for the time which stated that so long as your medical conditions were manageable and invisible, you need not fear any prejudice, except in conditions where you became the next epileptic patient, the next statistic in their list of patients with specific maladies and diagnoses.
To press this point even further, suffice it to say that to be a good doctor in the professional eye, your attentiveness, skill set, knowledge base, and physical abilities had to remain “normal” and intact. You had to be able to take a pulse with your fingers, listen to the beat of a heart and its audible signs of pathology, identify a medical problem based on visual black and white and color-based observations, and make the best, most accurate use your deductive reasoning and knowledge base on how to apply these observations to the cases at hand.
In the years ahead, while tutoring one of my blind students during my years as a chemistry professor in Oregon, I would very quickly learn that the possibility of being a blind physician was nil, and that you might be able to get away with having MS, at least for a while. Such was the prejudice medicine, medical schools and the teachers and administrators of these institutions had towards to disabled or handicapped. They never wanted to admit to this, nor will they do the same today, but cases demonstrating this is true are numerous, with exceptions way too scarce to be of much credibility, except as examples of a certain type of heroic achievement.
This view of the disabled attending school, in particular epileptics back in the 1980s, conflicted with other exceptions that existed to similar cases involved well rooted MDs. Should a doctor who became blind during the years of service, so long as you were working that part of the health care field in which your peers were not so uncomfortable about your limitations. A blind doctor could not read an EKG, but a physician with an active, uncontrolled seizure problem was always bait for all the malpractice attorneys administrators felt thrived out there, waiting to assist the next mistreated member of their surrounding community.
When I was involuntarily forced to “take medical leave” (disenroll) from a medical school in order to find the right treatment for my seizures, I was unaware of the limits they were placing on my abilities to find care. Fortunately I was able to search elsewhere for such care and in the end, enroll in a welfare program in a different state on the other side of the continent. This made it possible to receive the right forms of health care that I truly needed, provided by a skilled set of physicians specialized in epilepsy care, at another teaching hospital.
During the upcoming years, nearly two decades total, the forms of care provided to me ranged from the typical vocational rehab processes and free care provided to indigents by Good Samaritan Hospital. In a few months, I was a steady recipient of the limited financial services offered by the state welfare programs. To continue my Good Samaritan Hospital treatment, I was enrolled as patient #100 enrolled into a new Oregon Comprehensive Epilepsy Program (OCEP) that was developed as part of the nationwide neurosurgery programs being developed for treating and eliminating epilepsy. These were designed specifically to care for uncontrollable forms of epilepsy. The program I was enrolled in was housed in a new Neurosciences Building that had been built and opened just six months prior to my move to this state. After more than a year of care received as an unemployed dependent of the state’s welfare program by OCEP, I was finally enrolled in SSI and Medicaid for the first time nearly two years later. My experience with the government as it tried to better manage the care of low income people and families was just beginning.
A few years later, the Medicaid program switched from walk in services on a per need basis to a community health care model with a number of clinics established around the city for low income people to receive their care from. Even back then, preventive care was a goal of this program and the better management of patients in some sort of proactive way was the outcome many administrators were hoping for. My primary care provider during this time specialized in family medicine, a speciality only in the experimental stage during my years as a medical student a few years earlier. This primary care program went fairly well, although there were problems due to needs for a referral every time I had to see the seizure specialist, which was several times per year, and patients had to be willing to commit to engaging in the preventive care activities as they arose. This activity had little to no impacts on such things as smoking, drinking, the use of street drugs, and especially the use of the newest street drug for the time due to its homestyle manufacturing process–meth. AIDS had just become a major concern for people’s health outside to local street drug settings. Just around the corner in this part of the local timeline, an ADA conference was about to be held in the new Convention Center opened just outside of downtown Portland, with new lightrail tracks laid leading to its front doors next to the local Sports area.
A few years later, this Community Health care model was changed when Good Sam hospital was purchased by a large company overseeing the majority of health care facilities in the region. There were some changes in the care system that ensued, including the closure of my local community healthcare clinic due to the financial strains it placed on the system at large. This effectively removed me from receiving care from either my neurologist or my family medicine physician as I was reassigned to a new office, a new clinic located just down the road from where I lived. I was now beginning my new state-sponsored, funded and directed form of HMO-defined managed care.
Once again, a new plan for managing my personal health care was now in place. Preventive care goals were defined. Periodic visits were required. Regular and timely lab tests were now being performed. The honor system was still in place however when it came to following up on any recommendations that were made, like setting the next appointment date. It was up to me the patient to make sure my history of epilepsy never returned.
Fortunately, very soon after this change, I was finally able to return to the academic setting and managed to enroll in the local graduate school affiliated with the university where I was once a tutor, but now a researcher and instructor. As a graduate student, a lot of my health care could be received from clinicians on campus, but the transfer of my records to this office was a nightmare and so I stayed away from this option. I remained in this mixed clinical setting for the next 5 years, until 2002, when I received two masters degrees and then removed to New York, hoping to leave SSI and Medicaid behind forever.
Just before moving to New York, a Social Worker talked with me about possibly staying in Oregon due to the changes the state was making. Her hope was to hire me as an ombudsman for patients working in the state’s new social services program. Over the next few weeks this opportunity never came to be. The state was too slow in making any of the changes it had in mind.
Simultaneously with all of these health care services dilemmas, there was an ongoing political dispute I was having with the local, regional and national npos created for people with epilepsy. I tried to do my internship with one of their local offices, in order to learn more about how these npo’s operated. But they refused to afford me any such opportunity, so I went the route of earning an MPH in Health education/health promotion without the support of the disability service groups devoted to epilepsy-related issues. Form that point on I was learning the skills of population health, spatial epidemiology and biostatistics.
After moving to New York in late summer of 2002, I entered the spatial epidemiology, environmental health profession, specializing in epidemiology and the use of GIS for researching lyme disease, West Nile fever ecology, and population health, a look at the relationship between income level, poverty, quality of life, disease risk, and the differences between urban and rural health care provisions. One of the hot topics in the community health professions by then was social inequality, poverty and health. Having just come out of a decade of life in poverty and my dependency on Medicaid and SSI, I thought it was interesting to watch and listen as professors and classmates discussed their “wisdom” about what it was like to live in poverty during the months just before my removal to New York. Like many students and idealists, most participants in these classes felt they knew the answer to everything, such as why the many forms of physical and mental health problems exist within certain setting, or how to best manage an infectious disease epidemic. My feelings then were that these individuals lacked much if any experience living a life in poverty, evidence for which came out quite quickly each time one of these classroom discussion was held. This made me realize that during your academic years, you live one form of poverty that is distinctly different from the other forms that are out there in the real world. The pre-med and public health minded students, and the working class medical professionals taking most of these classes, bore similar attitudes and prejudices about the poor, none so bad as to be a career stopper, but to individuals like myself, these served as an interesting lesson in the power of the human ego, the lack of humility that often exists amongst the working class, and the simple prejudices against poverty and certain unhealthy living practices that remain to this day in the medical profession. Not once did I ever reveal to any of these students my own experiences with poverty and the difficulties of leaving the MCD-SSI-Sxn8 mazes replete with barely scalable fences and ladders behind.
Why share something like this with fellow students who are going to resist? ‘Only you could have such a unique view about this health care system,’ is what I told myself.
My experiences with Medicaid, SSI and Section 8 versus my experiences recently and now as a public health field worker taught me there are two forms of poverty out there to be understood by people–the temporary state of poverty experienced as part of the normal growing up process, and true poverty experienced as a part of one’s upbringing, happenstances and misfortune.
The first is a poverty experienced by almost everyone once the high school years are over. During those years you either choose to remain residing with your parents or you choose to find your own place and ultimately be responsible for all you own decisions about health, diet, drinking and how to spend your spare time running about, and ultimately how you accept your responsibility to work and to continue to grow career wise. During this time you choose to either take the easier career route and rely upon numerous short lived jobs here and there, whilst depending upon parents as well for those credit card bills, gas and electric bills, and monthly rents that inevitably become overdue.
The second is a form of poverty which only certain people wind up facing. It is not an occupational choice. It is or can be a result of that severe impoverished state one may be forced to live in for numerous social, parental or personal reasons, or it can be a period when one is forced to live in some impoverished state due to no choices he or she has ever made. Some such cases exist due to medical reasons, others due to personal choice as to how to survive and live out the rest of your life. Sometimes some sort of family-related issue or history of a parent forced to live the same lifestyle. Whatever the reason, it is the acceptance of this lifestyle as a part of your fate that ultimately becomes your reason for being this way. It is your personal choice, fostered and supported at times by your personal medical and socioeconomic history, as well as the same for your family and social events, that ultimately influences your decision as to whom and what to become. This reasoning is based on the Health Belief Model theory.
Very few students enrolled in the full time public health graduate program I was in had the luxury of also being enrolled in the federal programs for so many years, in the course of so many periods of change in this program. So, when these students expressed their solutions to better managing this system, it became quite apparent to me that few administrators and program planners, if any, had ever experienced the low income, non-academically linked lifestyle. Those hours of waiting spent in lines, seated in the waiting room on a broken chair, waiting for your number to be called by the next available social worker, is not a part of the experiences discussed within a classroom setting. Learning about a life in poverty and experiencing such a lifestyle are two very different things, one idealistic and even fabricated, the other more realistic but impossible to totally put into words.
When I moved New York, I had hopes of leaving SSI and Medicare behind. But the federal government very quickly knew where I was at and asked me to go to the closest SSI office in the nearby city, where we negotiated a way for me to search for work and deactivate my coverage and financial assistance they wished to continue to provide. I only remained in this region for about two years. As a whole, the region of New York is still very much behind the times when it comes to providing, monitoring and researching the new forms of cultural and population health care that need to be developed within the new health care system being promoted. The 2012 experience of health care will be very different from the 2013 experiences, and most likely, for many programs that are out there, it is back to the drawing board for most. There is this need for more imagination, knowledge and experience that will not be fully met for years to come unfortunately. The old silver baton with its plastic ends has yet to stored away, to make way for new technologies that are smaller, much lighter, and much more effective than the older way of doing things.
Silo Systems Incorporated
Old management styles, in combination with the idea of meeting years of old accreditation and performance requirements, result in poor outcomes when it comes to researching population health and improving the current health care systems performance. The health care programs that currently exist are often referred to as “Silo Systems”, a term that infers the different programs work independently of each other. Another way to conceptualize this is to consider that if you were someone who changed jobs every few years, there is this lack of continuity of care you have to contend with by changing doctors, insurance companies, and types of programs.
Such is not the case for someone who lives a life dependent on SSI, Section 8 and the various CHP and Medicaid coverage programs. If you have a lifetime disability, your record of health for your entire life is kept stored in governmental databases. Due to this, we have the ability to know the long term effects of being disabled your entire life. But should you be like many or most people, employable and managing to make ends meet, the current system is so out of order with itself that there is no way your doctor for a heart condition you develop when you reach 65 years of age is going to be able to look back to see how you were during your teen age years, much less your first years of developing your first signs of heart diseases to come. Medicine for the working class inherently produces the worst long term records of an individual’s health, and have doctors who know the least about their patients due to this lack of an adequate centralized personal health records database.
Good evidence for this claim are the hiring habits institutions have in this setting on the east coast. Like an old fashioned addictible drug care program, old positions are filled with new people, but rarely new thoughts or new ideas. On the east coast, according to west coast HR people and other hirers or employers, there is this tendency for Silo health care programs along the east coast to hire clinicians to do the statistical work. But analyzing population health is not the same as calculating cardiac output, IV flow rates, or making tables depicting heart rates, blood pressures and respiratory rates for you patients. Descriptive statistics may fill the need for the annual re-accreditation processes, but do little to improve upon overall population health related features, or facilitate change in any way related to health.
When we look at the problems that develop due to hiring someone for clinical credentials and its impact upon re-accreditation scores, instead of hiring individuals with the most needed skills, what we find is a program that can provide good care, but unable to exactly how good that care really is. This gets worse when we add cost for care to this scenario. By missing the statistical signs for a need for early intervention, because you focused too much on the numbers and too little one where changes were needed, it can be said that in terms of cost analyses, people with four year degrees mostly as clinicians, with some administrative background in their classroom and floor experience, can wind up being poor analysts for true prevalence rates, predictability indices, and recognizing when and how numerically low can still be used to measure and result in clinical much improvements. Silo-driven thinking and analyses processes by rule lack heterogeneity in the analysis and reassessment process. The evidence for these mistakes are as follows: for the most part, programs rely mostly upon basic descriptive data to plan their future activities and interventions, thereby failing to develop effective, cost saving interventions, allowing for costs in healthcare to skyrocket. These problems are:
- Over-reliance upon descriptive statistics
- Under-reliance on measures that evaluate change
- Little to no reliance upon measures that evaluate change temporally and in terms of continuity
- Little to no reliance upon measures that evaluate the statistical value of any changes that are made, selecting the right equation sporting most appropriate ‘p-value’ so to speak
- Little to no reliance upon measures that evaluated the social and biological value of changes that are made
- Little to no reliance upon methods for applying and testing and re-testing the ‘p-values’ that result from the various studies
- No reliance upon neighborhood specific or spatial analysis intervention processes for studying these outcomes
- No reliance upon cultural or ethnicity-specific analysis techniques for use in evaluating the most important public health issues within given community settings to date.
Moreover, there is an unusual comparison between east coast and west coast health care industries due to the fact that dense populations allow for multiple companies to service the same region, making it nearly impossible for environment and health, or environmental health, to be analyzed spatially in terms of regions, such as neighborhoods, villages, townships, and counties. In this way, the east coast ironically is very much behind the times when it comes to producing effective care and then providing adequate proof for its effectiveness. Rarely can a part of the east coast be evaluated regionally for health, due to HIPAA and lack of inter-corporate data sharing capabilities. The large population density of the region in combination with these silo generated problems are the reasons for this. They make it for difficult for east coast communities to develop any efficient data transferral process involving an up to data health information network (HIN). Two big hospitals can be just a few miles apart from each other and not share patient health data with each other due to the present HIPAA-phobia. This lack of efficient networking at the public health level is detrimental to population health in general, and can result in the following:
- The inability to perform long term studies due to lack of continuity within patient records
- Lack of sufficient areal databases for performing large area, high population density epidemiological reviews and studies
- Lack of an adequate, integrated health information network (HIN), with abilities to go above the standard state, federal, NIH or CDC related research and evaluation processes, and into a combination of qualitative and quantitative reviews making use of an electronic information network accessible to regional researchers.
- Lack of sufficient technological upgrades, providing the resources and tools needed to perform such analyses
- Lack of data use sufficiency and efficiency, due to lack of knowledge and experience
- Lack of any signs of conversion of these techniques into a method and system more indicative of high end spatio-temporal modeling of these population health statistics outcomes
Now in part I am so critical like this due to my opportunities to see population health being mapped out at the regional and national levels. By reviewing about one third of the population of some regions, statistically it is now possible to predict the final true stats based on a 33% sample. This is due to the simple rules of doubling, which state that if a population N has a certain result, the only way you can see a result that is statistically different, assuming true sampling is again repeated, is by doubling that population N to 2N. A ten percent change in 100,000,000 people, will not demonstrate significant impact even when 10,000,000 are added, because those 10,000,000 are selected randomly, and will disperse randomly across the total curve. With this in mind, a number of years ago I developed this way to model population-regional health using a 3-dimensional outcomes mapping algorithm on such large populations, and tested these numbers and results for smaller and smaller populations on down to 1% original N (1 million). A well dispersed sample has a particular shape that it develops with the age-gender pyramids, and it is this shape that defines your program needs. By applying this method of presenting disease, one can see where the greatest needs exist for such outcomes or activities as:
- an improved quality of care based upon HEDIS and NCQA like evaluations
- cost reductions, both overall costs and cost per disease class or type
- increases and decreases in health care services for specific communities, service providers
- the better management of low income population disease settings healthwise and in terms of prevention
- the development of a specialized, culturally-specific intervention program in order to more accurately impact target populations, preventively, palliatively and in terms of quality of life.
- creating a way to monitor disease states and prevent outbreaks
In recent attempts to initiate the use of this mapping technique as an outsider vying for a research position in a number of companies with the kind of data required to engaged in large area population health analysis, I experienced the corporate attitudes theorized to exist by many recent writers, especially those involved with the anti-big business, anti-corporation movement– the lack of interest in changing a system that has been around for so long, one which these companies have become so heavily reliant upon, even though it brings them closer and closer to economic and public health related failures.
During the course of my discussions now for about a year with big businesses, I have come to understand their different approaches to how they approach the questions that existed related to better understanding the health of a population. The methods they rely upon typically foster the same misguided uses for unique opportunities to better understand people, in terms of health, not as a consumer. This focus on money that seems so essential to operating Big Business in fact lacks long term goals and a long term vision.
The marketplace is more unstable, more actively fluctuating than population health. Over a several decade period, the marketplace can experience numerous ups and downs, five to tens times as many major changes and fluctuations in money flow than the corresponding changes in health of a population. In general, population health within a given region changes due to major changes in in-migration and out-migration patterns, those subtle changes in the population pyramid that take place due to people living longer and making their way into new age groups, some resulting in major shifts in the cost for their care. But none of these shifts in care or cost for car match up to the fluctuations that occur when big business institutes an across the board change in its membership eligibility or co-pay requirements, its eligibility for specific forms of care or whatever major changes are decided to be essential in terms of processes and the availability of specific care regimens. These changes appear to be substantial, which at the personal (patient) level they are. But in the corporate minds they are simply modifications of where the money goes, as part of a financial flow that is subject to even greater changes every time major mistakes are made corporation and business wide in the business world. Companies are in tune with themselves mostly, not the health of their patients.
The following kind of regional comparison can be made between two regions and their health care industries. Small regional businesses are of course better than nationwide large area sponsors or overseers of medical services. But small regional businesses can fall for the same corporate behaviors and non-patient-centered attitude that prevail in the medical insurance/regional care providers’ LLC world. The silos of knowledge that serve the inner company quite well, but which prevent much progress from being made, are what make it at times impossible for people to find a health care system capable of caring them from birth to death. Unfortunately, this is only possible in the low income Medicaid world for now, where people who are born into poverty die in poverty as well, either at an earlier age than those who are employed, and who switch health care insurers a half dozen times in their life if not more, of who go uninsured and therefore never leave medical researchers with much of a footprint detailing their life’s medical travels.
New York Public Health
The New York population setting is a dense mixture of poor and rich. Back around the year 1999, I did a population study of the eastern half of the state in order to focus on Hudson Valley health geography. The demographics map I produced had this unusual appearance of being a negative image of the typical archery or rifle range target with large rings alternating between black and white in order to depict the different scores. My population maps demonstrate alternating high and low income dense regions to be in the core of Manhattan, include the Parkside buildings changing to Harlem communities and such. Once you left the Manhattan proper, you were once again in higher income settings, due to the cost of the housing there, but as you headed further outward and away from the city edge, you passed through the subsidized housing ghettos situated all along the roadways and river’s edge. Next you passed through an upper middle income set of neighborhoods, followed by their low income community/welfare dependent matches, and then into the lower ends of the more upriver counties, where annual taxes, train stations, proximity to city work settings and cost of living and travel made for higher rents, followed by lower rents more upstream, followed by higher rents once again, and then lower rents. The lower income communities were represented by the black and low income white and other racial communities mostly, and the upper level income communities for each of these sectors primarily by communities consisting mostly of white families. The black rings on the related map I produced were formed by higher population densities of the minorities.
Within the heavily populated Hudson Valley, there are many overlapping regions of insurance agency coverage. The insurance provided to the rich by one company overlaps almost completely with the lower cost forms of care, but with higher premiums, offered to middle income communities residing in this same region. For the lower income communities, the distributions of cost and service are fairly equivalent across the entire region. This dense population does have smaller sections with exceptionally more claims filed for particular medical problems linked to quality of life, the need for urgent and emergent care, the lack of self-engaging forms of preventive health care, the need for more governmentally sponsored financial aids programs and much more of the various forms of unhealthy practices that exist, ranging from malnourishment, chronic disease problems, alcohol induced medical needs, the history of drug use or abuse, to a lack of adequate child care, an increase in family abuse behavioral practices, the rise in abandoned children, teenage runaways and suicides, etc. etc..
Examples of culturally-rich regions are few and far between in the New York setting, but a number of very important examples exist. There are several places where social determinants for disease and disease behaviors have potentially strong religious ties. Kiryas Joel stands out as an example, a completely Hassidic Jewish community setting occupied predominantly by very low income families. Like the Amish community in Pennsylvania this setting affords opportunities to explore family genetics and certain chronic and psychiatric disease traits. Culturally bound syndromes are not well documented in this section, and heritage related behaviors and practices that are potentially ICD-linked are in need of investigation. Likewise a number of traditional Russian Judaic communities exist in the Catskill hinterland settings.
Another type of socioeconomic region heavily investigated for its population health is the impoverished mountain community settings of the Adirondack region in upstate New York. A Regional HIN set up in upstate New York north of Albany and Troy would facilitate the ability to engage in this form of population health monitoring.
Managing such a heavily populated region requires a very robust database system that is well interlinked in order to share data fairly and responsibly between competitive health care provider companies, facilities and regional programs. Knowing the nuances in data entry behaviors such a variety of companies display, the need for a unified data entry and databasing process is just as important as developing a system which allows for information imports and exports in both the care setting and the various analyst’s settings that exist.
Unfortunately, for now, the state of the New York Health Information Network (HIN), even at a very local level such as in the form of a Manhattan and periphery, or Manhattan to Long Island, or Hudson Valley region, for the time being is not even developed at some theoretical or superficial level. This means that spatially analyzing New York Health Care for the time being is more a theoretical, sample-logic based analysis at best, a hit and miss piece of guesswork at most, and more than likely a small numbers related probabilities form of analysis for much of the region.
With less than one percent the population size of a densely populated state like New York, the western states, excluding California, have a much greater potential for producing a high quality HIN and developing a very comprehensive region public health population health monitoring program at the regional, state or large area substate level. In spite of lower numbers, statistical laws favor the results developed from a full population assessment formula, versus the many sampling methods that exist for east coast settings where the database producers represent a sample for a region but not a true population cross section. Even with regional insurance companies existing in the western country with overlapping regions, the presence of small numbers makes it possible for data sharing between two competitive companies to become a standard practice. The advantages with this scenario are several.
First, regional population health, when it crosses the quarter million or half million members range, makes it possible for year by year age-gender health behavior relationships to be better understood. We can define at exactly which age interventions need to commence in teen age girls with a high pregnancy or STD rate. We can determine when asthma first becomes a major adult onset medical problem due to environment (a spatial-areal feature) and behaviors (an age-gender feature) down to the community or neighborhood and one year age level. With smaller populations, sampling is not an issue, so the results calculated are true results, not simple guesswork. If we consider for example a case of Hanta Virus spread, for example from Arizona to Oregon, we can accurately map the rate of spread (which was done via my research lab setting) and then find a rate of host-vector migration. If a given urban setting has a higher percentage of Native American or Hispanic communities, we can accurately map out prevalence rates relative to place and determine how much a role urban patterns and automobile exhaust, or local climate and foliage patterns play a role in the develop of well localized emergency visits. In western settings, a complete environmental analysis become possible due to the use of a single health information network to store this population health information in.
Second, as just eluded to, more effective preventive health programs can be defined due to a very detailed HIN dataset. We know exactly at what ages teenage smoking and alcoholism reach their peaks, and why women require two intervention periods, the first at 15 years of age on up, the second at about 25-35 years of age, when those still smoking are bound to increase in numbers and smoking rates, at an age when prevention reduced the high costs that begin to be generted at 45 years of age, exponentially increasing from that point on in their lives.
Third, prediction modeling is more likely to be accurate when the full dataset is available. Some might argue against this claiming that compared with the other datasets, much larger in size but representing a smaller cross section of a population within a heavily population region, provide more statistical power due to the larger N. The million members rule pertains to these larger settings as well, but if ethnic diversity and diversity of lifestyles and income levels increase, these samples become questionable, since they are just samples of a region. Unless all agencies involved with the region and all patients are included in such analyses, we cannot make exceptionally reliable predictions about patient health without further insights into other larger areas of this country as well. Prediction modeling is favored in terms of percentages out west, not so much back east, and has a greater probability for failure back east due to people and business-generated sampling rules. If only the middle income range is analyzed in the east, due to confidentiality wants and needs for the upper income families covered, error is more likely to creep into the evaluations. Full datasets require full analyses without sampling, even within bigger regions and population settings.
Fourth, exceptionally long temporal studies of individual, family and even cultural health are possible in the west versus back east. This is due to the isolation of populations and regions in the west versus the east. Western population research opportunities offer more examples of physically and culturally isolated community setting. This provides the western public health researchers a greater opportunity for exploring previously unexplored topics such as culturally-bound syndromes, small region and topographical isolation derived health-income differences at the community level, and small region-large region opportunity differences that might exist related to cost reductions or deferences.
Fifth, by focusing on isolated population settings, with members relatively speaking in small numbers, we have for the first time an opportunity to engage in lifelong population health monitoring and management. People residing in community settings where health information exchange is possible, in spite of business or corporation differences, provides us with opportunities to see how major life changes impact individual lives. The quality and types of care offered to the poor child raised in a low income abusive household, who enters college and becomes insured by a company rather that a federal insurer, and who then retires and goes onto a mixed insurance program opportunity, can be monitored and compared regionally as small groups and as a much larger group with a middle income family raised worker, versus a person destined to remain in the low income setting for his/her entire life. Throughout all of these studies comparison can be made at the managed care, quality of care, level and type of service and interventions level, without ever having to use a sampled population technique involving matched pairing, ranking etc.
to be continued